The Big (and Small) Picture of Data Center Automation

Even though many of the details surrounding the future data center are still up in the air, it seems clear that data infrastructure will evolve along a number of rapidly emerging trend lines.

For one thing, the data center will be both larger and smaller as hyperscale facilities come to dominate centralized resources and the edge opens up to micro configurations. And virtually every rote task that currently takes up most technicians’ time will be automated.

As frightening as the idea sounds to many IT pros today, automation is unavoidable. Whether resources are housed in a multi-acre warehouse or distributed across geographic regions, it’s becoming evident that machines will be more adept at day-to-day monitoring and management than people.

Researchers at the University of Pisa, Italy, in fact, claim that the only practical way to push hyperscale to its optimal size is to rely completely on analytical, automated systems – no humans on-site at all. The team advocates “Autonomics 2.0,” says The Stack’s Martin Anderson, which encompasses a data-driven, predictive and holistic operating model in which logs and other event records spur machine learning systems to regulate the data environment and make changes to shifting workloads. Naturally, the study was conducted on Google’s hyperscale Cloud Platform using advanced analytics by the company’s BigQuery tool to crunch more than 12 TB of data on more than 11 billion rows.

On the micro side, of course, the sheer number of boxes and the distances between them all but rule out manned operation. According to Markets and Markets, the micro-mobile data center market will grow at an average annual pace of about 30 percent for the rest of the decade, jumping from $1.7 billion in 2015 to $6.3 billion by 2020. Many of these facilities will provide edge computing for fast-moving Big Data and IoT applications, using as little as five racks of equipment or even less depending on the advancements in converged infrastructure in the coming years. Aside from the occasional hardware swap, much of the management will be done remotely by automated systems.

But it’s not just the nature of infrastructure that will fuel automation, but the increased dynamism of the workload. As organizations increasingly adopt the DevOps model of the software-defined data center (SDDC), the emphasis on continuous integration/continuous development (CI/CD) will grow to the point where human operators can no longer keep up with the changing demands placed on applications and services. This is why many will turn to automated DevOps tools like Perspica’s Incident Replay platform, says EnterpriseTech’s George Leopold, to bring operations analytics to monitoring, troubleshooting and diagnostics. The system incorporates both machine learning and predictive analytics to provide visibility into network topologies, resource relationships and other components of the DevOps stack, overseeing literally millions of metrics to support optimal service delivery.

Even legacy data centers will not be immune to the automation bug. Cisco is out with a new system called Tetration that utilizes artificial intelligence to optimize data flows across multiple layers of infrastructure. Built partially on technology acquired from CliQr, the system seeks to bridge the divide between infrastructure and services management by mapping the relationships and resource dependencies defined by Layer 2 networking policies and other inter-app communications higher up the stack, says Data Center Knowledge’s Scott Fulton III. In many cases, these relationships are too complex for the human mind to fathom, so the only way to leverage legacy infrastructure for emerging digital processes is to hand it over to automation.

In all of the arguments over automation and jobs, one salient fact is often overlooked: While automation can in fact perform many tasks that humans cannot do, or cannot do as well, there are also many tasks that humans can do that automation can’t. Despite all the hoopla about artificial intelligence and processors that mimic the human brain, in the end a machine is still a machine and will never match the judgement, intuition and creativity that people bring to the table.

Any enterprise that deploys automation simply to lower payroll costs is not leveraging the technology to its full extent. But by the same token, any IT tech who does not embrace automation in a meaningful way will see their personal value to their organization diminish over time.

The happiest medium is where the enterprise, the automation stack and human resources work collectively to push their productivity to new levels.

Arthur Cole writes about infrastructure for IT Business Edge. Cole has been covering the high-tech media and computing industries for more than 20 years, having served as editor of TV Technology, Video Technology News, Internet News and Multimedia Weekly. His contributions have appeared in Communications Today and Enterprise Networking Planet and as web content for numerous high-tech clients like TwinStrata and Carpathia. Follow Art on Twitter @acole602.